The Cryosphere
The Cryosphere
TC
Articles | Volume 16, issue 11
Articles | Volume 16, issue 11
https://doi.org/10.5194/tc-16-4593-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-4593-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 11
Research article
 | 
03 Nov 2022
Research article |  | 03 Nov 2022

A random forest model to assess snow instability from simulated snow stratigraphy

Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer

Viewed

Total article views: 3,771 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,545 1,115 111 3,771 201 237
  • HTML: 2,545
  • PDF: 1,115
  • XML: 111
  • Total: 3,771
  • BibTeX: 201
  • EndNote: 237
Views and downloads (calculated since 10 Mar 2022)
Cumulative views and downloads (calculated since 10 Mar 2022)

Viewed (geographical distribution)

Total article views: 3,771 (including HTML, PDF, and XML) Thereof 3,571 with geography defined and 200 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Nov 2025
Download
Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
Read more
Share